Step 0) If you have tensorflow or tensorflow-gpu already and they are not working. I would advise to uninstall them both. Make sure you uninstall protobuf too just in case.
If you already have Python, skip to Step 2.

Step 1) First, make sure you install Python. Specifically Tensorflow requires Python 3.5 or 3.6. Go for one, but not both.
Add it to your PATH environment variable in the installation process. Also you can allow the maximum PATH length to exceed its normal limit.https://www.python.org/downloads/release/python-350/
This will help you use the Pip installation tool over the Windows Powershell. Install Pip over the Powershell with:python get-pip.py
And upgrade pip with:python -m pip install –upgrade pip

Step 2) Second, install CUDA Toolkit 8.0 at https://developer.nvidia.com/cuda-80-ga2-download-archive
Remember, 8.0, not 9.0. If you have downloaded 9.0 on accident, uninstall it over control panel (there are several programs that were installed, just remove one at a time).
There are two parts to installing 8.0. A Base Installer and a Patch. Run the Base Installer first, then the Path. Use express installation to avoid any troubles with looking to see if you have the correct drivers and files later. After installation, restart your computer and make sure you that the CUDA PATHS are set to the CUDA V8.0 Toolit directories.

Step 3) Now download and extract the cuDNN v6.1 files from https://developer.nvidia.com/cudnn.
For this, you first need to create a free account. Then, once logged in, follow the above link and click download. Find “Download cuDNN v6.0 (April 27, 2017), for CUDA 8.0“, click it, then click the “cuDNN v6.0 Library for Windows 10” link. The page should look like the page in the image below:
Download and extract the Library in any folder, INSTALLATION_FOLDER, that you choose. Then, go to your Windows Environment Variables settings, and add “INSTALLATION_FOLDER\cuda\bin” to the PATH environment variable (this folder should have “cudnn64_6.dll” in it). For example, if your INSTALLATION_FOLDER is “C:\Important_Files”, then you will add “C:\Important_Files\cuda\bin” to your PATH. (For a tutorial on adding directories to your PATH, see here or just do a Google search.).

Step 5) Open up a Windows Powershell with Administrator Privileges, and input the following command to install Tensorflow-GPU (for GPU compatible version).
>> pip install –upgrade tensorflow-gpu
Hopefully, at this point you have not gotten any errors. If you have problem installing tensorflow-gpu, it might be a problem with Python or pip rather than from the previous steps.
Check if Tensorflow-Gpu has been successfully installed following commands directly from the Tensorflow installation page:

Step 6) At this point, you can check if your GPU can be recognized by Tensorflow-GPU.
The commands over the Powershell are:python
>> from tensorflow.python.client import device_lib
>> device_lib.list_local_devices()

If it shows your CPU and GPU (like in the image below), then it looks like you are good to go!!

Note: If you get some issues with your GPU not being detected, then somewhere in the above steps (probably 2-4) there may have been a path or installation error.
Also, if you mistakenly installed both Tensorflow and Tensorflow-GPU, you should uninstall Tensorflow and Protobuf, then only install Tensorflow-GPU.

A wonderful experience in Vienna and in Europe in general!
Met several experienced researchers on Cyber-Physical Systems and had the opportunity to present a research project I helped work on.
I look forward to publishing and presenting more research in the future!

The NDSS 2016 Conference, held in San Diego on February 22-24th, was an amazing experience.
During the presentation sessions, I learned about the current problems in cyber-security and how the security community evaluates research.
In addition, I had the chance to present a poster (see link below). With the presentation, I received some valuable feedback on the project
and how we can improve it in the future.

An awesome highschool-university bridge program has been created and it is called ACHIEVE UC.
It is meant for helping high school students at San Diego High School to University of California – San Diego.
I attended an event which presented this program to the students for the first time. I was so excited to be back at my old school.
In addition, I spoke with around 600 students about my experiences as a homeless student and how beneficial this program will be for them.
I believe that it is possible for any student to go to a strong university through effort and dedication!
These students should have the belief and support that despite all their challenges that they face, they should be able to overcome them and attend university.
Let’s all hope these students will take advantage of this program, work hard, and earn great success!